Summary of Learning to Extract Structured Entities Using Language Models, by Haolun Wu et al.
Learning to Extract Structured Entities Using Language Models
by Haolun Wu, Ye Yuan, Liana Mikaelyan, Alexander Meulemans, Xue Liu, James Hensman, Bhaskar Mitra
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel approach to information extraction, redefining the task as entity-centric rather than triplet-centric. The authors propose the Approximate Entity Set OverlaP (AESOP) metric for evaluating model performance from diverse perspectives. They also develop the Multistage Structured Entity Extraction (MuSEE) model, harnessing the power of Language Models for enhanced effectiveness and efficiency by decomposing the extraction task into multiple stages. Experimental results demonstrate that MuSEE outperforms baselines in both quantitative and human evaluations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Information extraction is crucial in natural language processing. This paper takes a new approach to this problem, focusing on entities rather than triplets. The authors introduce a new way of evaluating model performance called AESOP. They also create a new model that breaks down the task into smaller parts, making it more efficient and effective. The results show that their model performs better than others. |
Keywords
* Artificial intelligence * Natural language processing
